Compressed sensing for rapid late gadolinium enhanced imaging of the left atrium: A preliminary study

Abstract

Current late gadolinium enhanced (LGE) imaging of left atrial (LA) scar or fibrosis is relatively slow and requires 5–15 minutes to acquire an undersampled (R=1.7) 3D navigated dataset. The GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) based parallel imaging method is the current clinical standard for accelerating 3D LGE imaging of the LA and permits an acceleration factor ~R=1.7. Two compressed sensing (CS) methods have been developed to achieve higher acceleration factors: a patch based collaborative filtering technique tested with acceleration factor R~3, and a technique that uses a 3D radial stack-of-stars acquisition pattern (R~1.8) with a 3D total variation constraint. The long reconstruction time of these CS methods makes them unwieldy to use, especially the patch based collaborative filtering technique. In addition, the effect of CS techniques on the quantification of percentage of scar/fibrosis is not known. We sought to develop a practical compressed sensing method for imaging the LA at high acceleration factors. In order to develop a clinically viable method with short reconstruction time, a Split Bregman (SB) reconstruction method with 3D total variation (TV) constraints was developed and implemented. The method was tested on 8 atrial fibrillation patients (4 pre-ablation and 4 post-ablation datasets). Blur metric, normalized mean squared error and peak signal to noise ratio were used as metrics to analyze the quality of the reconstructed images, Quantification was performed on the undersampled images and compared with the fully sampled images. Quantification of scar from post-ablation datasets and quantification of fibrosis from pre-ablation datasets showed that acceleration factors up to R~3.5 gave good 3D LGE images of the LA wall, using a 3D TV constraint and constrained SB methods. This corresponds to reducing the scan time by half, compared to currently used GRAPPA methods. Reconstruction of 3D LGE images using the SB method was over 20 times faster than standard gradient descent methods.

Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers

“…Towards this end, we utilized a referenceless "blur factor" metric that was used to estimate the extent of image blurring on a single‐image basis. The blur factor was proposed for evaluating natural images and has previously been used in MRI also . This metric applies a low‐pass filter to the test image and calculates the differences between the filtered image and the original image, normalized to the intensity of the original.…”

3D Dixon water-fat LGE imaging with image navigator and compressed sensing in cardiac MRI

“…Such 2D approaches may suffer from slice misregistration (incomplete breath-holding), artifacts due to respiratory motion, and constraints in spatial resolution [ 3 ]. Alternatively, 3D imaging has been proposed with the potential advantage of isotropic resolution and extended myocardial coverage, allowing for assessment of scar tissue in thinner myocardial walls like the atria or the right ventricle [ 4 – 6 ]. Previously reported 3D LGE sequences, however, suffered from long breath-hold duration > 20 s [ 7 ], thereby compromising the efficiency in potentially vulnerable patients [ 8 ].…”

Body diffusion‐weighted imaging using magnetization prepared single‐shot fast spin echo and extended parallel imaging signal averaging

“…In this supporting figure, the ss-MGOT image is considerably sharper than the nCPMG SS-FSE image while maintaining similar robustness against B 0 inhomogeneities. This sharpness is quantified using a blur factor described in (35) and applied to MRI images in (36). A line plot comparison over the grid in the phase encode direction is also given.…”